Airline Crew Scheduling Optimization

Description

This project implements optimization algorithms for solving airline crew scheduling problems using Genetic Algorithms (BGA, IBGA) and Simulated Annealing (SA). The goal is to efficiently allocate flight crew members while minimizing costs and maintaining operational constraints.

Table of Contents

• Installation
• Usage
• Configuration
• File Structure
• Contributing
• License

Installation

1. Clone the repository:

   git clone https://github.com/lorenzo1285/EC_CA.git
   cd EC_CA

2. Install dependencies:

   pip install -r Docs/requirements.txt

Usage

Run the Standard BGA optimization:

python Code/StandarBGA_102.py

Run the Improved BGA optimization:

python Code/ImprovedBGA_104.py

Run the Simulated Annealing optimization:

python Code/SA_102.py

Configuration

Modify the parameters inside each Python script to adjust optimization settings such as:

num_generations = 100
population_size = 50
mutation_rate = 0.1

File Structure

EC_CA/
│── Code/              # Python implementations of different optimization algorithms
│   ├── ImprovedBGA_104.py
│   ├── SA_102.py
│   ├── StandarBGA_102.py
│── Data/              # Input datasets for the optimization models
│   ├── sppnw41.txt
│   ├── sppnw42.txt
│   ├── sppnw43.txt
│── Docs/              # Documentation and references
│   ├── chu98.pdf
│   ├── Lecture4_StochasticLocalSearch-3.pdf
│   ├── requirements.txt  # Lists dependencies for the project
│   ├── Solving Air Crew Scheduling Problems using Simulated Annealing and Genetic Algorithms.pdf

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit changes (git commit -m "Add new feature").
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.